JuliaDubov/churn111

Model Trained Using AutoTrain

• Problem type: Tabular regression

Validation Metrics

• r2: 0.75551694767912
• mse: 0.06038101818653434
• mae: 0.12572727079081708
• rmse: 0.24572549356250023
• rmsle: 0.1700195996741877
• loss: 0.24572549356250023

Best Params

• learning_rate: 0.19966547950225813
• reg_lambda: 1.1910980465515898e-05
• reg_alpha: 0.003345407176272181
• subsample: 0.5134686751829827
• colsample_bytree: 0.7469701482100698
• max_depth: 7
• early_stopping_rounds: 407
• n_estimators: 15000
• eval_metric: rmse

Usage

import json
import joblib
import pandas as pd

model = joblib.load('model.joblib')
config = json.load(open('config.json'))

features = config['features']

# data = pd.read_csv("data.csv")
data = data[features]

predictions = model.predict(data)  # or model.predict_proba(data)

# predictions can be converted to original labels using label_encoders.pkl